CN111130630A - Communication satellite frequency spectrum monitoring equipment and frequency spectrum acquisition and feature identification method thereof - Google Patents

Communication satellite frequency spectrum monitoring equipment and frequency spectrum acquisition and feature identification method thereof Download PDF

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CN111130630A
CN111130630A CN201911419758.8A CN201911419758A CN111130630A CN 111130630 A CN111130630 A CN 111130630A CN 201911419758 A CN201911419758 A CN 201911419758A CN 111130630 A CN111130630 A CN 111130630A
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张更新
边东明
张景浩
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Nanjing Royal Communication Information Technology Co ltd
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    • H04—ELECTRIC COMMUNICATION TECHNIQUE
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    • H04B7/14—Relay systems
    • H04B7/15—Active relay systems
    • H04B7/185—Space-based or airborne stations; Stations for satellite systems
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
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    • H04B17/30—Monitoring; Testing of propagation channels
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B17/00—Monitoring; Testing
    • H04B17/30—Monitoring; Testing of propagation channels
    • H04B17/309—Measuring or estimating channel quality parameters
    • H04B17/345—Interference values
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04B—TRANSMISSION
    • H04B7/00—Radio transmission systems, i.e. using radiation field
    • H04B7/14—Relay systems
    • H04B7/15—Active relay systems
    • H04B7/185—Space-based or airborne stations; Stations for satellite systems
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Abstract

The invention discloses a communication satellite frequency spectrum monitoring device and a frequency spectrum acquisition and feature identification method thereof, belonging to the technical field of satellite communication and frequency spectrum monitoring; the device realizes long-term monitoring of GEO communication satellite signals, acquires and automatically stores spectrograms of all satellite signals in real time, completes statistical analysis of resource utilization and carrier distribution of all satellite signals, and provides a basis for resource scheduling and network optimization of a satellite communication system; the effective identification of satellite carrier signal parameters is realized, the validity check of the carrier signal is completed, the alarm is given in time when the abnormality is found, and the support is provided for the autonomous operation and troubleshooting of the system; the interference monitoring on satellite signals is realized, the accurate identification and automatic alarm of interference parameters are completed, and reference is provided for the anti-interference decision of the system. The method can simultaneously receive two paths of satellite downlink signals with the frequency range of 70-6000 MHz, and complete functions of frequency spectrum generation, parameter identification, interference monitoring and the like on the received signals.

Description

Communication satellite frequency spectrum monitoring equipment and frequency spectrum acquisition and feature identification method thereof
Technical Field
The invention belongs to the technical field of satellite communication and spectrum monitoring, and particularly relates to equipment suitable for a GEO communication satellite spectrum monitoring scene.
Background
In the present communication environment, especially in the satellite communication environment, the satellite signal is monitored in real time to obtain the frequency spectrum pattern and the signal parameters of the satellite transponder, so that the working state of the satellite transponder is analyzed, interference is judged, and the current available resource condition of the communication satellite is very important to grasp.
The existing solution mainly depends on a frequency spectrograph to obtain the frequency spectrum pattern and signal parameters of a satellite transponder, the real-time property of the existing solution is difficult to meet the requirements of some special application scenes, the automatic detection and alarm of interference cannot be completed, long-term real-time statistics on the transponder resource utilization condition cannot be achieved, and more users are relied on to carry out secondary analysis and judgment. And the equipment is heavy and the cost is higher. The present invention can effectively solve the above-mentioned disadvantages of the conventional solutions.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a communication satellite frequency spectrum monitoring device and a frequency spectrum acquisition and feature recognition method thereof aiming at the defects of the background technology, wherein a frequency spectrum graph of each satellite signal is acquired in real time and automatically stored, the statistical analysis of resource utilization and carrier distribution related to each satellite signal is completed, and a basis is provided for resource scheduling and network optimization of a satellite communication system; the effective identification of satellite carrier signal parameters is realized, the validity check of the carrier signal is completed, the alarm is given in time when the abnormality is found, and the support is provided for the autonomous operation and troubleshooting of the system; the interference monitoring on satellite signals is realized, the accurate identification and automatic alarm of interference parameters are completed, and reference is provided for the anti-interference decision of the system.
The invention adopts the following technical scheme for solving the technical problems:
a communication satellite frequency spectrum monitoring device comprises a case, a signal processing hardware platform and upper application software;
the system comprises a chassis, a frequency spectrum monitoring device and a frequency spectrum monitoring device, wherein the chassis is used for providing hardware support for the chassis-type frequency spectrum monitoring device;
the signal processing hardware platform is used for finishing amplification, filtering, frequency conversion and analog-to-digital conversion of input signals so as to convert analog signals into baseband signals;
and the upper application software is used for displaying the frequency spectrum data in a graphical mode, displaying characteristic parameters of the frequency spectrum data including frequency points and power, and meanwhile, carrying out secondary analysis, statistics and judgment on the frequency spectrum data.
As a further preferable scheme of the communication satellite spectrum monitoring device of the present invention, the chassis adopts a 1U standard chassis.
As a further preferred scheme of the communication satellite frequency spectrum monitoring device of the invention, the whole structure of the chassis adopts a frame type design, and a plurality of external interfaces are arranged behind the chassis, including 2 sets of transceiving interfaces, 1 external reference clock input interface, 1 set of standby transceiving interface, an RJ45 network port for external data interaction, a DB9 debugging interface and a power interface; a modularized power supply module is also arranged in the case and used for providing various voltages needed by the signal processing hardware platform.
As a further preferred scheme of the communication satellite spectrum monitoring equipment, the signal processing hardware platform adopts a series of full programmable SoC chips of Zynq-7000 of the sailing spring to complete the control of the machine box type spectrum monitoring equipment; the Kintex-7 series high-performance FPGA with rich resources is additionally arranged to complete the baseband signal and interface processing functions; the AD9361 chip of the high-performance transceiver of ADI company is adopted to realize the radio frequency and baseband front-end processing functions.
As a further preferable scheme of the communication satellite spectrum monitoring device of the present invention, the upper application software includes embedded core service software and client software;
the embedded core service software is used for completing functions of channel parameter configuration, planning scheduling and channel scheduling, acquiring frequency spectrum data and signal analysis data reported by the FPGA and realizing information interaction between a network communication interface and client software;
and the client software is used for realizing the presentation of the spectrogram of the current monitoring frequency band and the signal analysis result, providing a corresponding operation interface, finishing the functions of changing a monitoring plan, selecting a channel and switching a playback mode according to the operation of a user, and realizing information interaction by utilizing the Ethernet interface and the embedded core service software.
A method for obtaining baseband signal spectrum data and identifying characteristics specifically comprises the following two parts:
1) the broadband self-adaptive rapid separation frequency sweep algorithm is used for accurately acquiring the frequency spectrum characteristics of baseband signals and optimizing a dual-channel frequency spectrum data acquisition working mode, wherein the dual channels comprise a scanning channel and a resident channel;
2) a signal subspace-based short burst signal-to-noise ratio estimation algorithm is used for improving the base band signal cognitive ability of spectrum detection equipment in a satellite communication environment.
As a further preferable scheme of the method for obtaining baseband signal frequency spectrum data and identifying characteristics of the present invention, the broadband adaptive fast separation frequency sweep algorithm is specifically as follows:
step 1.1, roughly scanning by adopting a larger resolution bandwidth by utilizing a scanning channel to obtain the overall outline characteristics of a signal on a transponder;
step 1.2, dividing the whole frequency band to be scanned into a plurality of relatively independent groups according to the overall outline characteristics of the signals acquired in the step 1.1; adopting a resident channel for each group, acquiring fine frequency spectrum data, considering whether the resident channel is further divided into smaller groups according to the frequency spectrum of the resident channel, if so, adjusting the frequency resolution of the resident channel, analyzing again, and so on, and finally analyzing and clearing each signal;
step 1.3, dividing the spectrum adaptive area into three areas, specifically a signal or interference area, a noise area and a suspected area, based on the step 1.1 and the step 1.2; the frequency points in the signal area are signals, the frequency points in the noise area are noise, and the frequency points in the suspected area are undetermined.
As a further preferable scheme of the method for acquiring baseband signal spectrum data and identifying characteristics of the present invention, step 1.3 is specifically as follows:
the self-adaptive region division algorithm is mainly divided into three steps: the method comprises the following steps of filtering the frequency spectrum mean value, generating a histogram and delimiting a noise line signal line, and specifically comprises the following steps:
step 1.31, carrying out frequency spectrum mean filtering, wherein the mean filtering is carried out by convolving an original frequency spectrum by using a mean window with an odd length so as to obtain a mean spectrum;
step 1.32, generating a histogram by using the mean value spectrum data;
and step 1.33, marking out noise lines and signal lines by adopting the mean value spectrum histogram.
As a further preferable scheme of the method for obtaining baseband signal spectrum data and identifying characteristics of the invention, the signal-to-noise ratio estimation algorithm of the short burst signal based on the signal subspace specifically comprises the following steps:
an algorithm for estimating the signal-to-noise ratio is obtained by separating the signal and the noise from the received signal through the eigenvalue decomposition of the signal covariance matrix, and the algorithm can adapt to the channel characteristics; the method comprises the following specific steps:
step 2.1, decomposing the vector space of the received baseband signal into a signal subspace and a noise subspace;
step 2.2, decomposing the rank of the covariance matrix through searching;
step 2.3, the covariance matrix is formed by adopting signals received for the last N times by using a moving average method;
the implementation equation is as follows:
Figure BDA0002352034210000031
wherein y (k) ═ y (k), y (k +1), …, y (k + L-1)]HFor the kth reception of the baseband signal sequence, the length is L, H represents the complex conjugate, and N is the total number of receptions. The covariance matrix RyyDecomposing the characteristic value to obtain
Ryy＝U∑U (2)
Where ∑ diag { λ1,λ2,…,λLRank (λ) as a covariance matrix1,λ2,…,λL) A diagonal matrix of composition satisfying lambda1≥λ2…≥λLU is formed by standard orthogonalized eigenvectors and is the order number of a covariance matrix, and the order number is 40-100;
two information content criteria are employed to estimate the rank of the covariance matrix:
Figure BDA0002352034210000041
wherein, log represents taking the logarithm,
Figure BDA0002352034210000042
in order to be a function of the likelihood,
Figure BDA0002352034210000043
the method comprises the following steps of carrying out maximum likelihood estimation on parameter vectors, wherein pi represents a product, and sigma represents summation;
separate write to AIC and MDL guidelines
Figure BDA0002352034210000044
Figure BDA0002352034210000045
The estimated value of rank p is
Figure BDA0002352034210000046
Wherein min represents taking the minimum value;
whereby the noise power can be estimated according to the following equation
Figure BDA0002352034210000047
Signal power
Figure BDA0002352034210000048
And received signal-to-noise ratio
Figure BDA0002352034210000049
Figure BDA00023520342100000410
Figure BDA00023520342100000411
Figure BDA00023520342100000412
Carrying out simulation analysis on the performance of the SB signal-to-noise ratio estimation algorithm in an MATLAB environment, carrying out 100 times of simulation experiments, and selecting a commonly used digital modulation mode in satellite communication to test the estimation performance of the algorithm; the order number of the covariance matrix is L-40;
the accuracy of the snr estimation algorithm is measured by how far the estimated value deviates from the true value, and a mathematical criterion-standard deviation-is chosen that reflects this criterion well:
Figure BDA0002352034210000051
wherein the content of the first and second substances,
Figure BDA0002352034210000052
e represents the averaging, which is an estimate of the true signal-to-noise ratio SNR.
Compared with the prior art, the invention has the following remarkable advantages:
1. the invention realizes the long-term distributed monitoring of satellite signals, acquires and automatically stores the spectrograms of all the satellite signals in real time, completes the statistical analysis of the resource utilization and the carrier distribution of all the satellite signals and provides a basis for the resource scheduling and the network optimization of a satellite communication system;
2. the invention realizes effective identification of satellite carrier signal parameters, completes validity check of carrier signals, gives an alarm in time when abnormality is found, and provides support for autonomous operation and troubleshooting of a system;
3. the invention realizes the interference detection of all monitored satellite signals, completes the accurate identification and automatic alarm of interference parameters and provides reference for the anti-interference decision of the system;
4. the invention has the monitoring plan configuration capability, and determines the monitoring range of the monitoring plan by changing the monitoring center frequency and the monitoring bandwidth; the method has the spectrum monitoring data playback capability, and the playback has the functions of fast forward, rewind and pause; the method has the function of automatically uploading the frequency spectrum data; convenient to carry, the cost is lower.
Drawings
FIG. 1 is a schematic diagram of the signal processing hardware platform of the present invention;
FIG. 2-1 is a simulation diagram of the broadband adaptive fast separation frequency sweep algorithm-spectral mean filtering according to the present invention;
FIG. 2-2 is a simulation diagram of the broadband adaptive fast separation frequency sweep algorithm-histogram generation of the present invention;
FIG. 2-3 is a simulation diagram of the broadband adaptive fast separation frequency sweep algorithm-noise line signal line delineation of the present invention;
FIG. 3-1 is a signal subspace SNR estimation algorithm-signal subspace SNR performance simulation diagram according to the present invention;
FIG. 3-2 is a signal subspace SNR performance simulation diagram, which is an estimation algorithm for the signal subspace-based short burst signal SNR of the present invention;
3-3 is a simulation diagram of the signal subspace SNR estimation performance of the short burst signal SNR estimation algorithm based on the signal subspace of the present invention;
FIG. 4-1 is an embedded core services software-logic module architecture of the present invention;
fig. 4-2 is a client software-logic module architecture of the present invention.
Detailed Description
The invention is described in further detail below with reference to the figures and the detailed description.
The invention discloses a chassis type frequency spectrum monitoring device suitable for a GEO (geostationary orbit) communication satellite, belonging to the technical field of satellite communication and frequency spectrum monitoring; the device realizes long-term monitoring of GEO communication satellite signals, acquires and automatically stores spectrograms of all satellite signals in real time, completes statistical analysis of resource utilization and carrier distribution of all satellite signals, and provides a basis for resource scheduling and network optimization of a satellite communication system; the effective identification of satellite carrier signal parameters is realized, the validity check of the carrier signal is completed, the alarm is given in time when the abnormality is found, and the support is provided for the autonomous operation and troubleshooting of the system; the interference monitoring on satellite signals is realized, the accurate identification and automatic alarm of interference parameters are completed, and reference is provided for the anti-interference decision of the system. The method can simultaneously receive two paths of satellite downlink signals with the frequency range of 70-6000 MHz, and complete functions of frequency spectrum generation, parameter identification, interference monitoring and the like on the received signals.
The equipment mainly comprises the following parts: (1) the 1U standard case provides an equipment structure and a power supply; (2) a signal processing hardware platform, which is a radio frequency and baseband processing platform based on a high-performance transceiver AD9361 of the Sailing Zynq-7000 series SoC + Kintex-7 series FPGA + ADI company; (3) the core algorithm is used for operating an algorithm for completing monitoring data processing in the FPGA and the ARM; (4) and the upper application software runs in the computer to finish spectrum display, storage and parameter identification.
The main work flow of the equipment is as follows: the GEO communication satellite signal is input to a hardware platform through an SMA interface, impedance matching, amplification filtering, orthogonal down-conversion and analog-to-digital conversion are carried out on the signal, monitoring data received by the hardware platform are subjected to spectrum processing through a core algorithm, and then the monitoring data are transmitted to a computer running upper-layer application software through a network cable; and the upper application software completes the identification of the signal parameters, the display and storage of the signal spectrogram and the characteristic parameters and the like. The device supports the parallel monitoring of two paths of satellite signals.
The specific embodiment is as follows:
1. signal processing hardware platform
As shown in fig. 1, the signal processing hardware platform adopts the GPPA technology, and can perform spectrum analysis on two paths of signals simultaneously. Adopting a series of Sailing Zynq-7000 full programmable SoC chips (dual-core ARM + FPGA architecture) to complete system control; the Kintex-7 series high-performance FPGA with rich resources is additionally arranged to complete the baseband signal and interface processing functions; the AD9361 chip of the high-performance transceiver of ADI company is adopted to realize the radio frequency and baseband front-end processing functions. The platform has abundant hardware resources, including three groups of radio frequency transceiving channels, sufficient baseband processing resources (Zynq + FPGA), and abundant peripherals and interfaces, and is a spectrum analysis processing hardware platform with multichannel spectrum signal processing capability, high performance and strong expandability.
2. Broadband self-adaptive rapid separation frequency sweep algorithm
In order to accurately acquire the signal spectrum characteristics and optimize the dual-channel (scanning channel and resident channel) spectrum data acquisition working mode, a broadband adaptive rapid separation frequency sweep algorithm is provided. The realization idea is as follows: firstly, a scanning channel is used to perform coarse scanning by adopting a larger resolution bandwidth, signal characteristics on a transponder (even a wider bandwidth) are obtained, and the whole frequency band to be scanned is divided into a plurality of relatively independent 'groups' according to the overall 'outline' characteristics. For each group, adopting a resident channel to obtain fine spectrum data, considering whether the data is further divided into smaller groups according to the spectrum, if necessary, adjusting the frequency resolution of the resident channel, analyzing again, and so on, and finally analyzing and clearing each signal. The clustering process improves the analysis efficiency to some extent, but still requires a good compromise (not an infinite subdivision) between analysis efficiency and analysis accuracy.
Adaptive region partitioning divides the frequency spectrum into three regions: signal or interference regions (hereinafter referred to as signal regions), noise regions, and suspected regions. The frequency points in the signal area must be signals (or interference); the frequency points in the noise area must be noise; and undetermined frequency points in the suspected area. The design has the advantages that the suspected area can be judged by using the amplitude information of the frequency points and the surrounding environment information, namely the relation between the suspected area and the surrounding determined area (signal area or noise area), so that the judgment accuracy and the toughness of the algorithm are greatly improved.
The device adopts a self-adaptive region dividing method based on a histogram, and the method analyzes the histogram distribution of the spectrum data by means of the statistical characteristics of the filtered spectrum data so as to determine signal lines and noise lines. And the use of the average filtering removes a large amount of fluctuation in the frequency spectrum, and improves the accuracy of region delineation.
The self-adaptive region division algorithm is mainly divided into three steps: spectral mean filtering, histogram generation and noise line signal line delineation.
2.1 spectral mean Filtering
Mean filtering refers to using a length of LmeanA (typically odd) mean window convolves the original spectrum to obtain a mean spectrum. The mean filtering simulation is shown in fig. 2-1. As can be seen, the mean filtering removalIrrelevant fluctuation exists in the frequency spectrum, signals and noise are distinguished more obviously, and the statistical characteristics of the signals and the noise are amplified, so that good conditions are created for subsequent calculation. The ordinate in the figure uses true values instead of logarithmic values.
2.2 histogram Generation
Histogram generation refers to the generation of a histogram using mean spectral data, as shown in fig. 2-2.
2.3 noise line Signal line demarcation
The marking of the noise line and the signal line refers to marking the noise line and the signal line by using a mean value spectrum histogram.
Exemplary diagrams of the region adaptive partitioning are shown in fig. 2-3. In the upper picture, a green area is a noise area, a red area is a signal area, and a colorless area is an undetermined area. In the lower picture, the red line represents a signal line, and the green line represents a noise line.
3. Short burst signal-to-noise ratio estimation algorithm based on signal subspace
In order to improve the signal cognition ability of the frequency spectrum monitoring equipment in the satellite communication environment, a signal subspace-based short burst signal to noise ratio estimation algorithm is adopted.
The signal subspace-based (SB) algorithm is an algorithm which does not need a channel condition, separates a signal and noise from a received signal by eigenvalue decomposition of a signal covariance matrix and estimates a signal-to-noise ratio, and can adapt to channel characteristics.
The received signal vector space can be decomposed into a signal subspace and a noise subspace, which can be achieved by searching the rank of the covariance matrix. In practice, the covariance matrix can be re-evaluated by moving average method using the average formed by the last N received signals:
Figure BDA0002352034210000081
wherein y (k) ═ y (k), y (k +1), …, y (k + L-1)]HFor the kth reception of the baseband signal sequence, the length is L, H represents the complex conjugate, and N is the total number of receptions. The covariance matrixRyyDecomposing the characteristic value to obtain
Ryy＝U∑U (2)
Where ∑ diag { λ1,λ2,…,λLRank (λ) as a covariance matrix1,λ2,…,λL) A diagonal matrix of composition satisfying lambda1≥λ2…≥λLU is formed by standard orthogonalized eigenvectors and is the order number of a covariance matrix, and the order number is 40-100;
two information content criteria are employed to estimate the rank of the covariance matrix:
Figure BDA0002352034210000082
wherein, log represents taking the logarithm,
Figure BDA0002352034210000083
in order to be a function of the likelihood,
Figure BDA0002352034210000084
for maximum likelihood estimation of the parameter vector, Π represents the product and Σ represents the sum.
Separate write to AIC and MDL guidelines
Figure BDA0002352034210000091
Figure BDA0002352034210000092
The estimated value of rank p is
Figure BDA0002352034210000093
Wherein min represents taking the minimum value.
Whereby the noise power can be estimated according to the following equation
Figure BDA0002352034210000094
Signal power
Figure BDA0002352034210000095
And received signal-to-noise ratio
Figure BDA0002352034210000096
Figure BDA0002352034210000097
Figure BDA0002352034210000098
Figure BDA0002352034210000099
And (3) carrying out simulation analysis on the performance of the SB signal-to-noise ratio estimation algorithm in an MATLAB environment. And (5) carrying out 100 times of simulation experiments, and selecting a digital modulation mode commonly used in satellite communication to test the estimation performance of the algorithm. The order number of the covariance matrix is L-40.
The accuracy of the snr estimation algorithm is measured in terms of how far the estimated value deviates from the true value, where the mathematical criterion-standard deviation-is chosen that reflects this criterion well:
Figure BDA00023520342100000910
the QPSK modulation signal is formed by root raised cosine with roll-off factor of 0.35, carrier frequency of 12.8kHz, phase difference is generated randomly, symbol rate of 16ksps, sampling rate of 64kHz, total 1000 modulation symbols, and each symbol has 4 sampling points, and as can be seen from the simulation result given in figure 3-1, when the signal-to-noise ratio is greater than 4dB, the average estimation error is less than 0.1dB, and meanwhile, the signal-to-noise ratio estimation method based on signal subspace has very good universality, is insensitive to the frequency difference and the phase difference of the carrier, i.e. carrier synchronization does not need to be completed, and can also adapt well to the raised cosine modulation signal, and also does not need to complete bit timing, and the characteristic can meet the requirement of blind signal-to-noise ratio estimation with unknown signal related information in spectrum monitoring.
For TDMA signals, the number of symbols per slot is small, as shown in fig. 3-2 and 3-3, which show simulation results based on 100 symbols, and it can be seen that when the number of sampling points per symbol is 8, the estimation error is still less than 0.5 dB. Even with only 50 symbols, when the signal-to-noise ratio is greater than 0dB, the estimation error is still less than 0.5dB, and the requirement can still be met. The estimation accuracy of low signal-to-noise ratio can be obviously improved by increasing the number of sampling points of the symbols, and for the TDMA signals with short bursts and uncertain signal modulation modes, carrier synchronization and bit synchronization are not required to be completed, so that the method is suitable for frequency spectrum monitoring application.
4. Embedded core service software
Software description and main functions: the embedded core service software is compiled by using C/C + + language, is deployed in an ARM LINUX environment after being cross-compiled, completes functions of channel parameter configuration, plan scheduling, channel scheduling and the like, acquires frequency spectrum data and signal analysis data reported by the FPGA, and realizes information interaction between a network communication interface and client software.
Designing a logic module structure: as shown in fig. 4-1. After initializing all parameters, the parameter configuration module and the channel control module wait for receiving a thread scheduling module instruction together, after a user operates a client, the instruction completes the configuration of a data channel through the network communication module and the scheduling module to control the parameter configuration and the channel control module, the data processing module completes the processing of frequency spectrum data and signal analysis data reported by the data channel according to the related instruction, and the frequency spectrum data and the signal analysis data are sent to client software by the network communication module to complete the functions of display and the like. The equipment is provided with three physical channels, so that three different plans can be monitored simultaneously, and the scheduling module transmits required channel data to the client software according to the user requirements.
5. Client software
Software description and main functions: computer client software matched with the equipment runs on a host computer with a Java environment, realizes the presentation of a spectrogram of a current monitoring frequency band and a signal analysis result, provides a corresponding operation interface, and can complete the functions of monitoring plan change, channel selection, playback mode switching and the like according to user operation. And information interaction with the embedded core service software is realized in a wired mode.
Designing a logic module structure: as shown in fig. 4-2. The client software network communication module realizes information interaction with embedded core service software by using a UDP protocol, various sub-module functions are completed by the thread scheduling module, and the spectrum data processing module completes functions of spectral line shaping, background noise correction and the like according to spectrum data. The signal analysis and alarm processing module completes secondary screening of signal analysis results, the database service stores frequency spectrum data and signal analysis results, database service support is provided for replaying frequency spectrum monitoring data and reporting data, the operation instruction response module is presented as a user operation interface on the interface, and a user operation instruction is issued to the embedded core service software through the network communication module.

Claims (9)

1. A communications satellite spectrum monitoring device, characterized by: the system comprises a case, a signal processing hardware platform and upper application software;
the system comprises a chassis, a frequency spectrum monitoring device and a frequency spectrum monitoring device, wherein the chassis is used for providing hardware support for the chassis-type frequency spectrum monitoring device;
the signal processing hardware platform is used for finishing amplification, filtering, frequency conversion and analog-to-digital conversion of input signals so as to convert analog signals into baseband signals;
and the upper application software is used for displaying the frequency spectrum data in a graphical mode, displaying characteristic parameters of the frequency spectrum data including frequency points and power, and meanwhile, carrying out secondary analysis, statistics and judgment on the frequency spectrum data.
2. The communications satellite spectrum monitoring device of claim 1, wherein: the case adopts a 1U standard case.
3. The communications satellite spectrum monitoring device of claim 1, wherein: the whole structure of the case adopts a frame type design, and a plurality of external interfaces are arranged at the back of the case and comprise 2 groups of transceiving interfaces, 1 external reference clock input interface, 1 group of standby transceiving interfaces, an RJ45 network port for external data interaction, a DB9 debugging interface and a power supply interface; a modularized power supply module is also arranged in the case and used for providing various voltages needed by the signal processing hardware platform.
4. The communications satellite spectrum monitoring device of claim 1, wherein: the signal processing hardware platform adopts a series of full programmable SoC chips of the Zynq-7000 series to complete the control of the machine box type frequency spectrum monitoring equipment; the Kintex-7 series high-performance FPGA with rich resources is additionally arranged to complete the baseband signal and interface processing functions; the AD9361 chip of the high-performance transceiver of ADI company is adopted to realize the radio frequency and baseband front-end processing functions.
5. The communications satellite spectrum monitoring device of claim 1, wherein: the upper application software comprises embedded core service software and client software;
the embedded core service software is used for completing functions of channel parameter configuration, planning scheduling and channel scheduling, acquiring frequency spectrum data and signal analysis data reported by the FPGA and realizing information interaction between a network communication interface and client software;
and the client software is used for realizing the presentation of the spectrogram of the current monitoring frequency band and the signal analysis result, providing a corresponding operation interface, finishing the functions of changing a monitoring plan, selecting a channel and switching a playback mode according to the operation of a user, and realizing information interaction by utilizing the Ethernet interface and the embedded core service software.
6. The method for spectrum acquisition and feature recognition according to claims 1 to 4, comprising the following two steps:
1) the broadband self-adaptive rapid separation frequency sweep algorithm is used for accurately acquiring the frequency spectrum characteristics of baseband signals and optimizing a dual-channel frequency spectrum data acquisition working mode, wherein the dual channels comprise a scanning channel and a resident channel;
2) a signal subspace-based short burst signal-to-noise ratio estimation algorithm is used for improving the base band signal cognitive ability of spectrum detection equipment in a satellite communication environment.
7. The method for obtaining and identifying spectrum data of baseband signals according to claim 5, wherein the wideband adaptive fast separation frequency sweep algorithm is as follows:
step 1.1, roughly scanning by adopting a larger resolution bandwidth by utilizing a scanning channel to obtain the overall outline characteristics of a signal on a transponder;
step 1.2, dividing the whole frequency band to be scanned into a plurality of relatively independent groups according to the overall outline characteristics of the signals acquired in the step 1.1; adopting a resident channel for each group, acquiring fine frequency spectrum data, considering whether the resident channel is further divided into smaller groups according to the frequency spectrum of the resident channel, if so, adjusting the frequency resolution of the resident channel, analyzing again, and so on, and finally analyzing and clearing each signal;
step 1.3, dividing the spectrum adaptive area into three areas, specifically a signal or interference area, a noise area and a suspected area, based on the step 1.1 and the step 1.2; the frequency points in the signal area are signals, the frequency points in the noise area are noise, and the frequency points in the suspected area are undetermined.
8. The method according to claim 5, wherein the step 1.3 is as follows:
the self-adaptive region division algorithm is mainly divided into three steps: the method comprises the following steps of filtering the frequency spectrum mean value, generating a histogram and delimiting a noise line signal line, and specifically comprises the following steps:
step 1.31, carrying out frequency spectrum mean filtering, wherein the mean filtering is carried out by convolving an original frequency spectrum by using a mean window with an odd length so as to obtain a mean spectrum;
step 1.32, generating a histogram by using the mean value spectrum data;
and step 1.33, marking out noise lines and signal lines by adopting the mean value spectrum histogram.
9. The method according to claim 5, wherein the signal-to-noise ratio estimation algorithm for the short burst signal based on the signal subspace specifically comprises the following steps:
an algorithm for estimating the signal-to-noise ratio is obtained by separating the signal and the noise from the received signal through the eigenvalue decomposition of the signal covariance matrix, and the algorithm can adapt to the channel characteristics; the method comprises the following specific steps:
step 2.1, decomposing the vector space of the received baseband signal into a signal subspace and a noise subspace;
step 2.2, decomposing the rank of the covariance matrix through searching;
step 2.3, the covariance matrix is formed by adopting signals received for the last N times by using a moving average method;
the implementation equation is as follows:
Figure FDA0002352034200000031
wherein y (k) ═ y (k), y (k +1), …, y (k + L-1)]HFor the kth reception of the baseband signal sequence, the length is L, H represents the complex conjugate, and N is the total number of receptions. The covariance matrix RyyDecomposing the characteristic value to obtain
Ryy＝U∑U (2)
Where ∑ diag { λ1,λ2,…,λLRank (λ) as a covariance matrix1,λ2,…,λL) A diagonal matrix of composition satisfying lambda1≥λ2…≥λLU is formed by standard orthogonalized eigenvectors and is the order number of a covariance matrix, and the order number is 40-100;
two information content criteria are employed to estimate the rank of the covariance matrix:
Figure FDA0002352034200000032
wherein, log represents taking the logarithm,
Figure FDA0002352034200000033
in order to be a function of the likelihood,
Figure FDA0002352034200000034
the method comprises the following steps of carrying out maximum likelihood estimation on parameter vectors, wherein pi represents a product, and sigma represents summation;
separate write to AIC and MDL guidelines
Figure FDA0002352034200000035
Figure FDA0002352034200000036
The estimated value of rank p is
Figure FDA0002352034200000037
Wherein min represents taking the minimum value;
whereby the noise power can be estimated according to the following equation
Figure FDA0002352034200000041
Signal power
Figure FDA0002352034200000042
And received signal-to-noise ratio
Figure FDA0002352034200000043
Figure FDA0002352034200000044
Figure FDA0002352034200000045
Figure FDA0002352034200000046
Carrying out simulation analysis on the performance of the SB signal-to-noise ratio estimation algorithm in an MATLAB environment, carrying out 100 times of simulation experiments, and selecting a commonly used digital modulation mode in satellite communication to test the estimation performance of the algorithm; the order number of the covariance matrix is L-40;
the accuracy of the snr estimation algorithm is measured by how far the estimated value deviates from the true value, and a mathematical criterion-standard deviation-is chosen that reflects this criterion well:
Figure FDA0002352034200000047
wherein the content of the first and second substances,
Figure FDA0002352034200000048
e represents the averaging, which is an estimate of the true signal-to-noise ratio SNR.
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